SIR 2017 attendees on Wednesday afternoon can see how interventional radiologists at the University of California at Los Angeles (UCLA) use technology found in self-driving cars to power a machine learning application that helps guide patients’ interventional radiology care.
Kevin Seals, MD, David Geffen School of Medicine at UCLA, will present the abstract “Utilization of deep learning techniques to assist clinicians in diagnostic and interventional radiology: Development of a virtual radiology assistant” during the Healthcare Policy scientific symposium, which takes place from 3 to 4:30 p.m. Wednesday in Room 150A.
Using cutting-edge artificial intelligence to create a “chatbot,” interventional radiologists can automatically communicate with referring clinicians and quickly provide evidence-based answers to frequently asked questions. The referring physician can provide real-time information to the patient about the next phase of treatment or basic information about an interventional radiology treatment.
A small team of hospitalists, radiation oncologists and interventional radiologists at UCLA are using this prototype application now. The application resembles online customer service chats.
Dr. Seals and the rest of the UCLA team used a technology called Natural Language Processing, implemented using IBM’s Watson artificial intelligence computer, which can answer questions posed in natural language and perform other machine learning functions. The team developed a foundation of knowledge by feeding it more than 2,000 example data points simulating common inquiries interventional radiologists receive during a consultation.
Through this type of learning, the application can instantly provide the best answer to the referring clinician’s question in a conversational manner similar to text messaging. If the tool determines that an answer requires a human response, the program provides the contact information for a human interventional radiologist. As clinicians use the application, it learns from each scenario and progressively becomes smarter and more powerful.
The workings of the human brain inspired deep learning, where networks of artificial neurons analyze large datasets to automatically discover patterns and “learn” without human intervention. Deep learning networks can analyze complex datasets and provide rich insights in areas such as early detection, treatment planning and disease monitoring.
More about this abstract, No. 354, can be found at sirmeeting.org.